• DocumentCode
    2087038
  • Title

    A study of intrusion detection system based on data mining

  • Author

    Miao, Chunyu ; Chen, Wei

  • Author_Institution
    Coll. of Xingzhi, Zhejiang Normal Univ., Jinhua, China
  • fYear
    2010
  • fDate
    17-19 Dec. 2010
  • Firstpage
    186
  • Lastpage
    189
  • Abstract
    In this paper, classifications of intrusion detection and methods of data mining applied on them were introduced. Then, intrusion detection system design and implementation of based on data mining were presented. Such a system used APRIORI algorithm to analyse data association, which is the most influencing algorithm in mining Boolean association rules continuity item muster, with recurrence arithmetic based on idea of two period continuity item muster as core. Experiments showed that new type of attack can be detected effectively in the system, and knowledge base can be updated automatically, so the efficiency and accuracy of the intrusion detection were improved, and security of the network was enhanced.
  • Keywords
    Boolean functions; data mining; pattern classification; security of data; APRIORI algorithm; Boolean association rules mining; continuity item muster; data association; data mining; intrusion detection classification; intrusion detection system; network security; Analytical models; Correlation; Data mining; Data models; IP networks; Intrusion detection; data mining; intrusion detection; network security;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory and Information Security (ICITIS), 2010 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-6942-0
  • Type

    conf

  • DOI
    10.1109/ICITIS.2010.5688763
  • Filename
    5688763